AI-Based Solar Power Forecasting and Health Monitoring Techniques: Recent Trends and Challenges

Mohit Sahu

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 5, 565-576

Abstract: Solar energy has emerged as one of the most promising renewable energy sources due to its sustainability, abundance, and environmental benefits. However, the intermittent nature of solar power generation and system reliability challenges necessitate accurate forecasting and effective health monitoring techniques. Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), has significantly transformed solar energy systems by improving forecasting accuracy, fault detection, predictive maintenance, and energy storage optimization. This review paper explores recent advancements in AI-based solar power forecasting and health monitoring techniques, emphasizing models such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and hybrid frameworks. The study also examines AI-driven predictive maintenance approaches, including IoT-based monitoring, digital twin technologies, and thermal imaging methods for photovoltaic systems and battery energy storage systems. Furthermore, recent trends such as explainable AI, reinforcement learning, and smart grid integration are discussed. The paper highlights key challenges, including data quality, computational complexity, model generalization, and real-time deployment, while identifying future opportunities for intelligent and scalable solar energy management systems.

Keywords: Artificial Intelligence (AI); Solar Power Forecasting; Photovoltaic (PV) Systems; Health Monitoring; Machine Learning and Deep Learning (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2612578
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i5:id:2202

DOI: 10.5281/zenodo.23600231

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